{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from shutil import copyfile\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting / Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gaussian Process Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gpflow\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gpflow.utilities import print_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpflow.config.set_default_summary_fmt(\"notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MATLAB engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matlab.engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng = matlab.engine.start_matlab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matlab.engine.futureresult.FutureResult at 0x7fd3e206cd90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eng.load_system(\"../Simulink/polydome\", background = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load weather data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which experimental set to simulate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_id = 'Exp7'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the corresponding WDB to the model input location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../Data/input_WDB.mat'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyfile(f\"../Data/Experimental_data_WDB/{exp_id}_WDB.mat\", \"../Data/input_WDB.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>zenith</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>Tsky_rad</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>cloud_index</th>\n",
       "      <th>pressure</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>aoi</th>\n",
       "      <th>incidence_main</th>\n",
       "      <th>incidence_second</th>\n",
       "      <th>poa_direct</th>\n",
       "      <th>poa_diffuse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>201707132000</td>\n",
       "      <td>77.726968</td>\n",
       "      <td>288.783339</td>\n",
       "      <td>32.174594</td>\n",
       "      <td>84.913015</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>77.726968</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>6.839369</td>\n",
       "      <td>84.913015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>201707132005</td>\n",
       "      <td>78.534908</td>\n",
       "      <td>289.630841</td>\n",
       "      <td>167.597825</td>\n",
       "      <td>88.433972</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>78.534908</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>33.313565</td>\n",
       "      <td>88.433972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>201707132010</td>\n",
       "      <td>79.337991</td>\n",
       "      <td>290.480180</td>\n",
       "      <td>104.009389</td>\n",
       "      <td>82.377507</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>79.337991</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>19.243300</td>\n",
       "      <td>82.377507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900</td>\n",
       "      <td>201707132015</td>\n",
       "      <td>80.135916</td>\n",
       "      <td>291.331669</td>\n",
       "      <td>767.555215</td>\n",
       "      <td>33.326318</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>80.135916</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>131.491069</td>\n",
       "      <td>33.326318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200</td>\n",
       "      <td>201707132020</td>\n",
       "      <td>80.928349</td>\n",
       "      <td>292.185610</td>\n",
       "      <td>126.334677</td>\n",
       "      <td>70.317711</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "      <td>-9999</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96300</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>80.928349</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>19.919124</td>\n",
       "      <td>70.317711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time     timestamp     zenith     azimuth         dni        dhi  \\\n",
       "0     0  201707132000  77.726968  288.783339   32.174594  84.913015   \n",
       "1   300  201707132005  78.534908  289.630841  167.597825  88.433972   \n",
       "2   600  201707132010  79.337991  290.480180  104.009389  82.377507   \n",
       "3   900  201707132015  80.135916  291.331669  767.555215  33.326318   \n",
       "4  1200  201707132020  80.928349  292.185610  126.334677  70.317711   \n",
       "\n",
       "   OutsideTemp  Tsky_rad  relative_humidity  precipitation  cloud_index  \\\n",
       "0         24.0      18.0                 50          -9999          0.5   \n",
       "1         24.0      18.0                 50          -9999          0.5   \n",
       "2         24.0      18.0                 50          -9999          0.5   \n",
       "3         24.0      18.0                 50          -9999          0.5   \n",
       "4         24.0      18.0                 50          -9999          0.5   \n",
       "\n",
       "   pressure  wind_speed  wind_direction        aoi  incidence_main  \\\n",
       "0     96300           0           -9999  77.726968           -9999   \n",
       "1     96300           0           -9999  78.534908           -9999   \n",
       "2     96300           0           -9999  79.337991           -9999   \n",
       "3     96300           0           -9999  80.135916           -9999   \n",
       "4     96300           0           -9999  80.928349           -9999   \n",
       "\n",
       "   incidence_second  poa_direct  poa_diffuse  \n",
       "0             -9999    6.839369    84.913015  \n",
       "1             -9999   33.313565    88.433972  \n",
       "2             -9999   19.243300    82.377507  \n",
       "3             -9999  131.491069    33.326318  \n",
       "4             -9999   19.919124    70.317711  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wdb = pd.read_pickle(f\"../Data/Experimental_python/{exp_id}_WDB.pkl\")\n",
    "df_wdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Setpoint</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>SupplyTemp</th>\n",
       "      <th>InsideTemp</th>\n",
       "      <th>SolRad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:00:00+02:00</th>\n",
       "      <td>18.827586</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.933333</td>\n",
       "      <td>91.714933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:05:00+02:00</th>\n",
       "      <td>3628.965517</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>23.216667</td>\n",
       "      <td>121.538700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:10:00+02:00</th>\n",
       "      <td>4391.344828</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>23.116667</td>\n",
       "      <td>101.481617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:15:00+02:00</th>\n",
       "      <td>4392.466667</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>163.710150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:20:00+02:00</th>\n",
       "      <td>3777.482759</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.633333</td>\n",
       "      <td>90.039567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Power  Setpoint  OutsideTemp  SupplyTemp  \\\n",
       "timestamp                                                                   \n",
       "2017-07-13 20:00:00+02:00    18.827586      22.5         24.0        23.9   \n",
       "2017-07-13 20:05:00+02:00  3628.965517      22.5         24.0        15.5   \n",
       "2017-07-13 20:10:00+02:00  4391.344828      22.5         24.0        14.3   \n",
       "2017-07-13 20:15:00+02:00  4392.466667      22.5         24.0        14.0   \n",
       "2017-07-13 20:20:00+02:00  3777.482759      22.5         24.0        14.0   \n",
       "\n",
       "                           InsideTemp      SolRad  \n",
       "timestamp                                          \n",
       "2017-07-13 20:00:00+02:00   22.933333   91.714933  \n",
       "2017-07-13 20:05:00+02:00   23.216667  121.538700  \n",
       "2017-07-13 20:10:00+02:00   23.116667  101.481617  \n",
       "2017-07-13 20:15:00+02:00   22.800000  163.710150  \n",
       "2017-07-13 20:20:00+02:00   22.633333   90.039567  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle(f\"../Data/Experimental_python/{exp_id}_data.pkl\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment runtime: 554100\n"
     ]
    }
   ],
   "source": [
    "runtime = df_wdb['time'].iloc[-1]\n",
    "print(f\"Experiment runtime: {runtime}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulink"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the CARNOT simulation initial temperature `t0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng.workspace['t0'] = float(23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the CARNOT simulation air exchange rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_air = np.zeros([df_wdb.shape[0], 2])\n",
    "np_air[:, 0] = df_wdb['time']\n",
    "np_air[:, 1] = 2.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng.workspace['air_exchange_rate'] = matlab.double(np_air.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Heat Random Input Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pel_max = 6300\n",
    "COP_heating = 5.0\n",
    "COP_cooling = 5.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function for generating random signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_random_signal(nstep, a_range = (-1, 1), b_range = (2, 10), signal_type = 'analog'):\n",
    "\n",
    "    a = np.random.rand(nstep) * (a_range[1]-a_range[0]) + a_range[0] # range for amplitude\n",
    "    b = np.random.rand(nstep) *(b_range[1]-b_range[0]) + b_range[0] # range for frequency\n",
    "    b = np.round(b)\n",
    "    b = b.astype(int)\n",
    "\n",
    "    b[0] = 0\n",
    "\n",
    "    for i in range(1,np.size(b)):\n",
    "        b[i] = b[i-1]+b[i]\n",
    "        \n",
    "    if signal_type == 'analog':\n",
    "        random_signal = np.zeros(nstep)\n",
    "        # Random Signal\n",
    "        i=0\n",
    "        random_signal = np.zeros(nstep)\n",
    "        while b[i]<np.size(random_signal):\n",
    "            k = b[i]\n",
    "            random_signal[k:] = a[i]\n",
    "            i=i+1\n",
    "        return random_signal\n",
    "    elif signal_type == 'prbs':\n",
    "        \n",
    "        # PRBS\n",
    "        a = np.zeros(nstep)\n",
    "        j = 0\n",
    "        while j < nstep:\n",
    "            a[j] = a_range[1]\n",
    "            a[j+1] = a_range[0]\n",
    "            j = j+2\n",
    "\n",
    "        i=0\n",
    "        prbs = np.zeros(nstep)\n",
    "        while b[i]<np.size(prbs):\n",
    "            k = b[i]\n",
    "            prbs[k:] = a[i]\n",
    "            i=i+1\n",
    "            \n",
    "        return prbs\n",
    "    else:\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "len_signal = len(df_wdb)\n",
    "\n",
    "random_signal = get_random_signal(len_signal, a_range = (- COP_cooling * Pel_max, COP_heating * Pel_max), signal_type = 'analog')\n",
    "prbs = get_random_signal(len_signal, a_range = (0, 5), signal_type = 'prbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure() \n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(random_signal, drawstyle='steps',label='Random Signal')\n",
    "plt.legend()\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(prbs, drawstyle='steps', label='PRBS')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the original electric power consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "power = np.array([df_wdb['time'], random_signal]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the heating power by passing through a heating/cooling COP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng.workspace['power'] = matlab.double(power.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the simulation parameters and run it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng.set_param('polydome', 'StopTime', str(runtime), nargout = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng.workspace['result'] = eng.sim('polydome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpret the simulation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def carnot_to_series(eng, start_timestamp):\n",
    "\n",
    "    # Compile the simulation dict\n",
    "    dict_simulation = {}\n",
    "    dict_simulation['SimulatedTemp'] = np.asarray(eng.eval('result.SimulatedTemp.Data')).reshape(-1)\n",
    "    dict_simulation['time'] = np.asarray(eng.eval('result.SimulatedTemp.Time')).reshape(-1)\n",
    "    \n",
    "    # Create the dataframe from dict\n",
    "    df_simulation = pd.DataFrame(dict_simulation)\n",
    "    df_simulation.set_index('time', inplace = True, drop = True)\n",
    "    \n",
    "    # Define the timestamps and set it as index\n",
    "    df_simulation['timestamp'] = start_timestamp + df_simulation.index.map(lambda x: pd.Timedelta(seconds = x))\n",
    "    df_simulation = df_simulation.reset_index().set_index('timestamp')\n",
    "    \n",
    "    # Resample the dataframe to 5 min intervals\n",
    "    # Taking the mean when there are multiple points, padding with zero order when data is missing\n",
    "    df_simulation = df_simulation['SimulatedTemp'].resample('5min').mean().pad()\n",
    "    \n",
    "    return df_simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simulation = carnot_to_series(eng, df.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure() \n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(df_simulation.index, df_simulation, label = 'Simulated Temperature')\n",
    "plt.plot(df.index, df['OutsideTemp'], '-.', label = 'Outside Temperature')\n",
    "plt.title('Temperatures')\n",
    "plt.legend()\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(power[:, 1], drawstyle = 'steps', label = 'Heat Input')\n",
    "plt.title('Heat Input')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the outputs to the experimental df: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'SimulatedTemp'] = df_simulation\n",
    "df.loc[:,'SimulatedHeat'] = power[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power</th>\n",
       "      <th>Setpoint</th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>SupplyTemp</th>\n",
       "      <th>InsideTemp</th>\n",
       "      <th>SolRad</th>\n",
       "      <th>SimulatedTemp</th>\n",
       "      <th>SimulatedHeat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:00:00+02:00</th>\n",
       "      <td>18.827586</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.933333</td>\n",
       "      <td>91.714933</td>\n",
       "      <td>23.084581</td>\n",
       "      <td>4239.488599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:05:00+02:00</th>\n",
       "      <td>3628.965517</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>23.216667</td>\n",
       "      <td>121.538700</td>\n",
       "      <td>23.461498</td>\n",
       "      <td>4239.488599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:10:00+02:00</th>\n",
       "      <td>4391.344828</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>23.116667</td>\n",
       "      <td>101.481617</td>\n",
       "      <td>23.586236</td>\n",
       "      <td>4239.488599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:15:00+02:00</th>\n",
       "      <td>4392.466667</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>163.710150</td>\n",
       "      <td>23.675021</td>\n",
       "      <td>4239.488599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:20:00+02:00</th>\n",
       "      <td>3777.482759</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.633333</td>\n",
       "      <td>90.039567</td>\n",
       "      <td>23.764361</td>\n",
       "      <td>4239.488599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:35:00+02:00</th>\n",
       "      <td>9.517241</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.766667</td>\n",
       "      <td>3.260000</td>\n",
       "      <td>23.734483</td>\n",
       "      <td>10384.873952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:40:00+02:00</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.733333</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>23.806762</td>\n",
       "      <td>10384.873952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:45:00+02:00</th>\n",
       "      <td>9.137931</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.750000</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>23.806762</td>\n",
       "      <td>10384.873952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:50:00+02:00</th>\n",
       "      <td>4.206897</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.733333</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>24.008241</td>\n",
       "      <td>10384.873952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20 05:55:00+02:00</th>\n",
       "      <td>4.233333</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>24.032897</td>\n",
       "      <td>10384.873952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1848 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Power  Setpoint  OutsideTemp  SupplyTemp  \\\n",
       "timestamp                                                                   \n",
       "2017-07-13 20:00:00+02:00    18.827586      22.5         24.0        23.9   \n",
       "2017-07-13 20:05:00+02:00  3628.965517      22.5         24.0        15.5   \n",
       "2017-07-13 20:10:00+02:00  4391.344828      22.5         24.0        14.3   \n",
       "2017-07-13 20:15:00+02:00  4392.466667      22.5         24.0        14.0   \n",
       "2017-07-13 20:20:00+02:00  3777.482759      22.5         24.0        14.0   \n",
       "...                                ...       ...          ...         ...   \n",
       "2017-07-20 05:35:00+02:00     9.517241      22.5         22.0        23.6   \n",
       "2017-07-20 05:40:00+02:00     5.666667      22.5         22.0        23.6   \n",
       "2017-07-20 05:45:00+02:00     9.137931      22.5         22.0        23.6   \n",
       "2017-07-20 05:50:00+02:00     4.206897      22.5         22.0        23.6   \n",
       "2017-07-20 05:55:00+02:00     4.233333      22.5         22.0        23.6   \n",
       "\n",
       "                           InsideTemp      SolRad  SimulatedTemp  \\\n",
       "timestamp                                                          \n",
       "2017-07-13 20:00:00+02:00   22.933333   91.714933      23.084581   \n",
       "2017-07-13 20:05:00+02:00   23.216667  121.538700      23.461498   \n",
       "2017-07-13 20:10:00+02:00   23.116667  101.481617      23.586236   \n",
       "2017-07-13 20:15:00+02:00   22.800000  163.710150      23.675021   \n",
       "2017-07-13 20:20:00+02:00   22.633333   90.039567      23.764361   \n",
       "...                               ...         ...            ...   \n",
       "2017-07-20 05:35:00+02:00   22.766667    3.260000      23.734483   \n",
       "2017-07-20 05:40:00+02:00   22.733333    3.250000      23.806762   \n",
       "2017-07-20 05:45:00+02:00   22.750000    3.240000      23.806762   \n",
       "2017-07-20 05:50:00+02:00   22.733333    3.340000      24.008241   \n",
       "2017-07-20 05:55:00+02:00   22.800000    3.380000      24.032897   \n",
       "\n",
       "                           SimulatedHeat  \n",
       "timestamp                                 \n",
       "2017-07-13 20:00:00+02:00    4239.488599  \n",
       "2017-07-13 20:05:00+02:00    4239.488599  \n",
       "2017-07-13 20:10:00+02:00    4239.488599  \n",
       "2017-07-13 20:15:00+02:00    4239.488599  \n",
       "2017-07-13 20:20:00+02:00    4239.488599  \n",
       "...                                  ...  \n",
       "2017-07-20 05:35:00+02:00   10384.873952  \n",
       "2017-07-20 05:40:00+02:00   10384.873952  \n",
       "2017-07-20 05:45:00+02:00   10384.873952  \n",
       "2017-07-20 05:50:00+02:00   10384.873952  \n",
       "2017-07-20 05:55:00+02:00   10384.873952  \n",
       "\n",
       "[1848 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Process Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Gaussian Process Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = Path(Path.cwd(), 'model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_gpr(model_path):\n",
    "    x_scaler = pickle.load(open(Path(model_path, 'x_scaler.pkl'), 'rb'))\n",
    "    m_params = pickle.load(open(Path(model_path, 'gp_params.gpf'), 'rb'))\n",
    "    m_data = pickle.load(open(Path(model_path, 'gp_data.gpf'), 'rb'))\n",
    "\n",
    "    k = gpflow.kernels.SquaredExponential(lengthscales=([1] * m_data[0].shape[1])) + gpflow.kernels.Constant()\n",
    "\n",
    "    m = gpflow.models.GPR(\n",
    "        data = m_data, \n",
    "        kernel = k, \n",
    "        mean_function = None\n",
    "        )\n",
    "    \n",
    "    gpflow.utilities.multiple_assign(m, m_params)\n",
    "    \n",
    "    return x_scaler, m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaler, m = load_gpr(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name                              </th><th>class    </th><th>transform       </th><th>prior  </th><th>trainable  </th><th>shape  </th><th>dtype  </th><th>value                                             </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>GPR.kernel.kernels[0].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>672551.1934857197                                 </td></tr>\n",
       "<tr><td>GPR.kernel.kernels[0].lengthscales</td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>(7,)   </td><td>float64</td><td>[2.96352201e+05, 4.54452962e+05, 4.06450919e+02...</td></tr>\n",
       "<tr><td>GPR.kernel.kernels[1].variance    </td><td>Parameter</td><td>Softplus        </td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>676495.6894024109                                 </td></tr>\n",
       "<tr><td>GPR.likelihood.variance           </td><td>Parameter</td><td>Softplus + Shift</td><td>       </td><td>True       </td><td>()     </td><td>float64</td><td>0.10898288218871159                               </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_summary(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_to_gpr(df, lu = 2, ly = 3, dyn_in = 'SimulatedHeat', dyn_out = 'SimulatedTemp'):\n",
    "    \n",
    "    df_gpr = df[['OutsideTemp', 'SolRad', 'SimulatedTemp', 'SimulatedHeat']].copy()\n",
    "    df_gpr.rename(columns = {dyn_in: 'u', dyn_out: 'y'}, inplace = True)\n",
    "    \n",
    "    # Add the regressive inputs/outputs\n",
    "    for idx in range(1, lu + 1):\n",
    "        df_gpr.loc[:, f\"u_{idx}\"] = df_gpr['u'].shift(idx)\n",
    "\n",
    "    for idx in range(1, ly + 1):\n",
    "        df_gpr.loc[:, f\"y_{idx}\"] = df_gpr['y'].shift(idx)\n",
    "\n",
    "    df_gpr.dropna(inplace = True)\n",
    "    \n",
    "    return df_gpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OutsideTemp</th>\n",
       "      <th>SolRad</th>\n",
       "      <th>y</th>\n",
       "      <th>u</th>\n",
       "      <th>u_1</th>\n",
       "      <th>u_2</th>\n",
       "      <th>y_1</th>\n",
       "      <th>y_2</th>\n",
       "      <th>y_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:15:00+02:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>163.710150</td>\n",
       "      <td>23.675021</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>23.586236</td>\n",
       "      <td>23.461498</td>\n",
       "      <td>23.084581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:20:00+02:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>90.039567</td>\n",
       "      <td>23.764361</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>23.675021</td>\n",
       "      <td>23.586236</td>\n",
       "      <td>23.461498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:25:00+02:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>74.428250</td>\n",
       "      <td>23.810838</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>23.764361</td>\n",
       "      <td>23.675021</td>\n",
       "      <td>23.586236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:30:00+02:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>70.027167</td>\n",
       "      <td>23.881527</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>23.810838</td>\n",
       "      <td>23.764361</td>\n",
       "      <td>23.675021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 20:35:00+02:00</th>\n",
       "      <td>24.0</td>\n",
       "      <td>70.906667</td>\n",
       "      <td>24.481502</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>4239.488599</td>\n",
       "      <td>23.881527</td>\n",
       "      <td>23.810838</td>\n",
       "      <td>23.764361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           OutsideTemp      SolRad          y            u  \\\n",
       "timestamp                                                                    \n",
       "2017-07-13 20:15:00+02:00         24.0  163.710150  23.675021  4239.488599   \n",
       "2017-07-13 20:20:00+02:00         24.0   90.039567  23.764361  4239.488599   \n",
       "2017-07-13 20:25:00+02:00         24.0   74.428250  23.810838  4239.488599   \n",
       "2017-07-13 20:30:00+02:00         24.0   70.027167  23.881527  4239.488599   \n",
       "2017-07-13 20:35:00+02:00         24.0   70.906667  24.481502  4239.488599   \n",
       "\n",
       "                                   u_1          u_2        y_1        y_2  \\\n",
       "timestamp                                                                   \n",
       "2017-07-13 20:15:00+02:00  4239.488599  4239.488599  23.586236  23.461498   \n",
       "2017-07-13 20:20:00+02:00  4239.488599  4239.488599  23.675021  23.586236   \n",
       "2017-07-13 20:25:00+02:00  4239.488599  4239.488599  23.764361  23.675021   \n",
       "2017-07-13 20:30:00+02:00  4239.488599  4239.488599  23.810838  23.764361   \n",
       "2017-07-13 20:35:00+02:00  4239.488599  4239.488599  23.881527  23.810838   \n",
       "\n",
       "                                 y_3  \n",
       "timestamp                             \n",
       "2017-07-13 20:15:00+02:00  23.084581  \n",
       "2017-07-13 20:20:00+02:00  23.461498  \n",
       "2017-07-13 20:25:00+02:00  23.586236  \n",
       "2017-07-13 20:30:00+02:00  23.675021  \n",
       "2017-07-13 20:35:00+02:00  23.764361  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gpr = data_to_gpr(df)\n",
    "df_gpr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_input = df_gpr.drop(columns = ['u', 'y'])\n",
    "df_output = df_gpr['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_input = df_input.to_numpy()\n",
    "np_output = df_output.to_numpy().reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_input_sc = x_scaler.transform(np_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean, var = m.predict_y(np_input_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate performance of loaded model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 5))\n",
    "plt.plot(df_gpr.index, np_output[:, :], label = 'Measured data')\n",
    "plt.plot(df_gpr.index, mean[:, :], label = 'Gaussian Process Prediction')\n",
    "plt.fill_between(\n",
    "    df_gpr.index, \n",
    "    mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),from pathlib import Path\n",
    "\n",
    "from shutil import copyfile\n",
    "\n",
    "import pickle\n",
    "\n",
    "Load the general math/data manipulation packages\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "Load the packages related to the Gaussian Process Regressor:\n",
    "\n",
    "import gpflow\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from gpflow.utilities import print_summary\n",
    "\n",
    "gpflow.config.set_default_summary_fmt(\"notebook\")\n",
    "\n",
    "tf.config.set_visible_devices([], 'GPU')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Load the CasADi package used for optimization:\n",
    "\n",
    "import casadi\n",
    "\n",
    "Import MATLAB engine and start it in the background since this takes a while:\n",
    "\n",
    "import matlab.engine\n",
    "\n",
    "eng = matlab.engine.start_matlab()\n",
    "\n",
    "eng.load_system(\"../Simulink/polydome\", background = True)\n",
    "\n",
    "<matlab.engine.futureresult.FutureResult at 0x7f211ba8ec40>\n",
    "\n",
    "Copy the experimental data set to the CARNOT input location:\n",
    "\n",
    "    mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),\n",
    "    alpha = 0.2\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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}